A method for estimating the aboveground biomass of rice based on multi-spectral images of an unmanned aerial vehicle (UAV), including: normatively collecting UAV multi-spectral image data of rice canopy and ground measured biomass data; after collection, preprocessing images, extracting reflectivity and texture feature parameters, calculating a vegetation index, and constructing a new texture index; and by stepwise multiple regression analysis, integrating the vegetation index and the texture index to estimate rice biomass, and establishing a multivariate linear model for estimating biomass. A new estimation model is verified for accuracy by a cross-validation method. The method has high estimation accuracy and less requirements on input data, and is suitable for the whole growth period of rice. Estimating rice biomass by integrating UAV spectrum and texture information is proposed for the first time, and can be widely used for monitoring crop growth by UAV remote sensing.

A method for estimating the aboveground biomass of rice based on multi-spectral images of an unmanned aerial vehicle (UAV), including: normatively collecting UAV multi-spectral image data of rice canopy and ground measured biomass data; after collection, preprocessing images, extracting reflectivity and texture feature parameters, calculating a vegetation index, and constructing a new texture index; and by stepwise multiple regression analysis, integrating the vegetation index and the texture index to estimate rice biomass, and establishing a multivariate linear model for estimating biomass. A new estimation model is verified for accuracy by a cross-validation method. The method has high estimation accuracy and less requirements on input data, and is suitable for the whole growth period of rice. Estimating rice biomass by integrating UAV spectrum and texture information is proposed for the first time, and can be widely used for monitoring crop growth by UAV remote sensing.

The present invention relates to a method for protecting the ecology of a coastal mudflat and a system used in the method. The method includes the following steps: plan and design a pond project according to the specific conditions of the target coastal mudflat to be protected; dig a trench and build a dike in the surface of the target coastal mudflat and enclose the dike to form at least one pond, wherein the trench in the pond is adjacent to the surface of the mudflat to form the pond with a large water surface, a shallow water layer and a seasonal open waterfront; and cultivate an aquatic product in the trench, with or without an emergent aquatic plant planted in the surface of the mudflat.

Method for producing soluble potassium sulfate by recrystallization of crude potassium sulfate wherein the crude potassium sulfate contains an amount of potassium, calculated as K.sub.2O, of about 15 wt % or higher, and the resulting potassium sulfate crystalline material conforms with the following characteristics: the amount of insoluble material is less than about 0.05 wt %, a 1 wt % solution of the potassium sulfate has a pH below about 6, and/or 1 pH unit lower than the pH of the crude potassium sulfate, the fraction obtained after crystallization has an average particle size within the following parameters: (i) d90<about 0.6 mm, (ii) d10>about 0.02 mm, and (iii) dust amounts to about 0.4 wt % or less, whereby the resulting potassium sulfate contains more than 51% potassium, calculated as K.sub.2O.

Provided is an artificial inoculation method for bacterial panicle blight of rice. The method includes the steps of S1. preparing an inoculum using a bacteria solution of Burkholderia glumae; S2. inoculating at the booting stage of rice; S3. adopting an inoculation method of injection, and injecting the inoculum prepared in the step S1 into the hollow booting part of a rice stalk from bottom to top by an injector; and S4. calculating the incidence rate and disease index after inoculation according to the number of diseased panicles and the number of diseased grains.

Provided is an artificial inoculation method for bacterial panicle blight of rice. The method includes the steps of S1. preparing an inoculum using a bacteria solution of Burkholderia glumae; S2. inoculating at the booting stage of rice; S3. adopting an inoculation method of injection, and injecting the inoculum prepared in the step S1 into the hollow booting part of a rice stalk from bottom to top by an injector; and S4. calculating the incidence rate and disease index after inoculation according to the number of diseased panicles and the number of diseased grains.

A method for monitoring rice bacterial blight includes: obtaining a multi-spectral image, severities of the rice bacterial blight, and accumulated temperature data of a rice field at different growth stages; obtaining resistance of rice varieties to the bacterial blight; extracting a mean canopy spectral reflectance of each plot in the rice field; conducting regression of the severity of the rice bacterial blight using a convolutional neural network based on the mean canopy spectral reflectance and the severity of the rice bacterial blight, and outputting a depth spectrum feature; training a disease severity regression model with the accumulated temperature data, the depth spectrum feature, and the resistance to the bacterial blight for each plot as an input and the corresponding severity as an output; and monitoring a severity of the rice bacterial blight in a to-be-monitored rice field using the disease severity regression model.

A method for monitoring rice bacterial blight includes: obtaining a multi-spectral image, severities of the rice bacterial blight, and accumulated temperature data of a rice field at different growth stages; obtaining resistance of rice varieties to the bacterial blight; extracting a mean canopy spectral reflectance of each plot in the rice field; conducting regression of the severity of the rice bacterial blight using a convolutional neural network based on the mean canopy spectral reflectance and the severity of the rice bacterial blight, and outputting a depth spectrum feature; training a disease severity regression model with the accumulated temperature data, the depth spectrum feature, and the resistance to the bacterial blight for each plot as an input and the corresponding severity as an output; and monitoring a severity of the rice bacterial blight in a to-be-monitored rice field using the disease severity regression model.

Provided herein are methodology and composition for use of any nut (such as almond, walnut, or pistachio) or legume (peanut) shell and/or husk material in a growing substrate, with or without other components such as peat, perlite, or coir; for plant growth, whether it be used in its whole form or some reduced form such as, having been chipped or ground, and whether composted or not.

Provided herein are methodology and composition for use of any nut (such as almond, walnut, or pistachio) or legume (peanut) shell and/or husk material in a growing substrate, with or without other components such as peat, perlite, or coir; for plant growth, whether it be used in its whole form or some reduced form such as, having been chipped or ground, and whether composted or not.